
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "32"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 86 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 82 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.009056	3.871391	18.482482	13.943354	8.398362	11.942860	10.556991	11.163305	0.7124	0.7401	0.1827	14.005175	12.747950
2459593	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	29.402325	24.104279	10.477514	11.800213	15.402347	7.885089	5.235726	6.181740	0.6391	0.6680	0.1732	3.387374	3.720368
2459592	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.185814	-1.149845	1.203714	1.395356	-0.998252	-0.610617	-1.926032	0.025834	0.0290	0.0290	0.0010	nan	nan
2459591	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	35.375153	31.900438	13.104925	14.476190	5.078834	7.511360	1.579349	1.662995	0.5615	0.5961	0.2432	3.032184	3.224007
2459590	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	36.855391	32.692837	11.757960	13.284995	5.374305	4.106698	3.455777	4.734138	0.5624	0.5977	0.2391	4.317231	4.468411
2459589	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	35.481136	27.144896	10.589869	10.714381	3.981894	4.030017	5.991594	7.870616	0.6059	0.6421	0.2154	3.039924	3.459800
2459588	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	216.078222	215.749750	inf	inf	22.902962	25.608793	21.462675	19.695784	nan	nan	nan	0.000000	0.000000
2459587	dish_ok	100.00%	-	-	-	100.00%	0.00%	350.418501	351.698808	inf	inf	107.499264	110.188315	578.145724	532.067935	nan	nan	nan	0.000000	0.000000
2459586	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	22.872064	15.345221	0.879604	0.562836	6.571085	5.204907	18.644823	30.787133	0.5826	0.6246	0.2895	3.380385	3.452822
2459585	dish_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.095508	-1.019225	0.156265	0.895116	-0.562873	0.086794	-0.721337	0.494049	0.0326	0.0317	0.0009	0.000000	0.000000
2459584	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	43.575132	20.683671	5.955004	2.788531	4.059668	3.993980	4.094837	10.847284	0.5338	0.6105	0.3382	5.676425	7.643433
2459583	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	34.010629	13.336354	2.810169	1.327807	10.279468	12.730844	12.543739	9.661128	0.5611	0.6347	0.3487	4.157510	4.335705
2459582	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	34.258939	8.517548	2.544632	0.687899	3.719224	2.761571	3.125962	5.523974	0.5609	0.6477	0.3554	3.659026	3.774815
2459581	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459580	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	34.441106	2.523011	2.782837	0.160358	3.051657	1.138906	4.536445	7.680646	0.5702	0.6722	0.3668	4.378318	4.816412
2459579	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	35.483656	5.981149	3.487299	0.878536	4.503574	3.475902	3.658543	5.685952	0.5664	0.6623	0.3626	3.249964	4.051670
2459578	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.293737	-0.002769	-0.741714	-0.722469	0.117419	-1.013766	-0.596653	-0.441531	0.0303	0.0295	0.0010	nan	nan
2459577	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	41.652539	25.462904	2.670917	1.754808	4.905837	18.788000	5.652422	23.675339	0.5664	0.6296	0.3133	3.460382	3.447127
2459576	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	32.667062	13.974871	2.142044	1.189917	5.876657	14.351921	9.396989	30.294439	0.5857	0.6525	0.3158	2.670382	2.781079
2459575	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	23.855344	-0.000339	1.399996	0.204305	12.804474	-0.269316	2.642221	0.285138	0.6962	0.7489	0.3161	0.000000	0.000000
2459574	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	32.301395	3.629643	1.966282	0.230341	4.192956	5.471123	5.347782	11.913415	0.5989	0.6887	0.3460	3.561644	3.301380
2459573	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	30.593300	0.381292	3.508498	0.397459	14.694951	-0.770609	1.546196	0.073999	0.5769	0.6781	0.3785	0.000000	0.000000
2459572	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	33.121941	0.156472	1.923349	0.117363	2.065895	-0.567296	2.123690	0.086281	0.5727	0.6799	0.3795	4.832569	4.825312
2459571	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.062723	0.019629	-0.764368	-0.760333	1.853431	-0.162758	-1.189644	-0.402564	0.0299	0.0300	0.0011	nan	nan
2459570	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	43.571382	9.684418	11.168598	8.903732	3.770096	6.203417	4.893685	13.250407	0.5769	0.6792	0.3515	5.415271	6.127361
2459569	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	33.897793	2.001508	2.541278	0.256481	3.203901	2.650121	3.561969	1.887589	0.6860	0.7455	0.2253	4.460002	3.793713
2459566	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	34.731971	-0.138820	2.974230	0.338022	3.691172	-0.441553	2.189306	0.480540	0.5706	0.6820	0.3846	-0.000000	-0.000000
2459565	dish_ok	0.00%	-	-	-	100.00%	0.00%	0.000000	0.000000	-0.016407	-0.016407	-0.758305	-0.758305	-0.875717	-0.875717	nan	nan	nan	0.000000	0.000000
2459564	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	14.669094	0.963712	-0.311312	0.911606	-0.245326	-0.715732	0.303176	5.330080	0.5872	0.6788	0.3622	2.896865	2.674959
2459563	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459562	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	10.681610	0.060095	-0.645162	-0.027293	-0.846772	-1.450773	-0.438842	-1.240461	0.5823	0.6828	0.3657	3.309538	2.923682
2459561	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	10.058963	0.665316	-0.083666	0.501413	1.568568	-1.174346	0.195329	-2.008976	0.6028	0.6871	0.3666	3.587023	3.129015
2459560	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.077806	0.438877	-0.448344	0.319120	1.296573	-0.662855	-0.223698	-1.507035	0.5895	0.6804	0.3770	3.443673	2.978336
2459559	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	12.873420	0.937267	-0.042409	0.815546	-0.182652	-0.271751	-0.232996	-1.409148	0.5809	0.6886	0.3717	2.964658	2.823695
2459558	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	10.388000	4.248796	9.959868	10.230720	22.909161	12.029837	5.892383	-0.616789	0.6177	0.6730	0.3773	2.482069	2.774034
2459557	dish_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0320	0.0299	0.0010	nan	nan
2459556	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	14.108040	4.648919	9.993229	10.475665	17.638337	12.368605	19.206941	8.210053	0.6019	0.6716	0.3590	2.464541	2.624356
2459553	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.5770	0.6615	0.3653	nan	nan
2459552	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	21.053640	5.491279	12.636354	14.613140	17.962726	23.115774	0.849647	1.484863	0.5719	0.6672	0.3629	1.397802	1.292768
2459551	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.338182	0.630991	-0.362503	0.514425	-0.421167	-0.805804	-0.992557	-1.499233	0.5666	0.6799	0.3830	3.155291	2.983019
2459550	dish_ok	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0409	0.0398	0.0007	nan	nan
2459549	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459542	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	20.149414	4.275438	18.648638	21.675993	0.733228	1.133542	3.299964	8.513959	0.6365	0.7254	0.3313	3.466845	3.162271
2459541	dish_ok	100.00%	1.34%	1.34%	0.00%	100.00%	0.00%	21.629911	3.851233	11.530692	12.943713	8.545644	10.115490	4.789346	3.509205	0.6270	0.7176	0.3100	4.724701	4.329571
2459540	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.096247	3.362693	1.332957	1.504450	3.372451	2.146516	4.995358	4.449327	0.6584	0.7031	0.2931	3.275255	3.083049
2459536	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	22.862142	16.665279	15.548556	16.047633	19.215560	21.451077	11.994507	53.898406	0.6081	0.6476	0.2760	2.442179	2.594072
2459535	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.479536	2.407856	0.980832	1.340601	3.856167	2.019456	3.497861	4.294477	0.6940	0.7224	0.3238	2.630667	2.703214
2459534	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.002991	0.639474	0.148573	0.514882	4.060524	0.468517	10.319687	4.777178	0.7069	0.7396	0.3380	5.757262	6.882179
2459533	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.647243	4.730428	10.158922	11.007084	18.024863	10.290646	1.064104	-1.792317	0.6391	0.7023	0.3768	2.481108	2.654679
2459532	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	21.505001	5.009545	12.439729	13.860346	13.836571	18.075972	4.044963	9.358922	0.6010	0.6838	0.3591	2.713705	2.895107
2459530	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.370618	0.364275	-0.223931	0.456289	-0.133140	-0.607009	1.731384	0.569669	0.6295	0.7168	0.3648	2.495552	2.892967
2459527	dish_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	dish_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	4.591684	4.383989	6.157273	5.908497	5.093518	2.556962	0.574233	-0.287464	0.0344	0.0319	0.0012	nan	nan
2459508	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.564978	6.745424	8.995634	8.807025	16.400865	7.344586	9.143749	10.110728	0.8708	0.9118	0.2367	0.000000	0.000000
2459505	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	22.417677	4.957892	10.209276	10.513091	4.589736	5.725806	3.465372	13.274056	0.8054	0.8413	0.1733	6.263296	6.917073

Antenna Season Report Notebook¶

Antenna 32 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 32, Node 2:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 32: 2459610

Antenna 32: 2459609

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Antenna 32: 2459513

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Antenna 32: 2459506

Antenna 32: 2459505

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶